class_docstr: Abstract Layer for tensorflow_text tokenizers.
  
  Input shape:
    N-D (2D as default) tensor with shape: `(batch_size, input_length)`.
  
  Output shape:
    (N+1)-D (3D as default) tensor with shape:
      `(batch_size, input_length, token_dim)`.
docstr: Computes output shape for the layer.
  
  Args:
    input_shape: Shape tuple (tuple of integers) or list of shape tuples (one
      per output tensor of the layer). Shape tuples can include None for free
      dimensions, instead of an integer.
  
  Returns:
    Computed output shape(s).
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code: def compute_output_shape(self, input_shape):
      """Computes output shape for the layer.
  
      Args:
        input_shape: Shape tuple (tuple of integers) or list of shape tuples (one
          per output tensor of the layer). Shape tuples can include None for free
          dimensions, instead of an integer.
  
      Returns:
        Computed output shape(s).
      """
      input_shape = tf.TensorShape(input_shape).as_list()
      shape = [dim for dim in input_shape]
      # because the output of tokenization is ragged, the added dimension should
      # be set as None
      shape.append(None)
      return tf.TensorShape(shape)
  
imports: import os
  import tensorflow
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops.ragged import ragged_conversion_ops
  from tensorflow_text.python.ops import unicode_script_tokenizer
  from tensorflow_text.python.ops import whitespace_tokenizer
  from tensorflow_text.python.ops import wordpiece_tokenizer
label_desc: Use image node_id 5 for calling the TokenizerBase obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: tf